I started with the data model, pieces as objects with position and type, and a board as a 2D grid.
Start by clarifying the scope and requirements with the interviewer, then outline a high-level design covering data model, move validation, and game state management. Dive into trade-offs for key components like board representation and move generation, and discuss how you would scale or extend the system.
Pro tip: Emphasize the importance of separating the core game logic from the user interface and consider how you would handle edge cases like castling, en passant, and pawn promotion early in the design.
Ask questions to understand the scope: Is this a single-player or multiplayer game? Should it support AI? What are the performance and scalability needs? This ensures you design the right system.
Choose a representation for the board (e.g., 2D array, bitboards) and pieces, and define how to store game state including move history, turn, and special move flags.
Outline algorithms to validate moves for each piece, including special moves, and generate legal moves while considering checks and pins.
Describe how to manage turns, detect checkmate/stalemate, and update the board after each move. Consider undo/redo and persistence.
Compare design choices (e.g., bitboards vs. arrays) and discuss scalability, extensibility (e.g., variants), and integration with UI or APIs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business goal and constraints, then outline the end-to-end system: data collection, candidate generation, ranking, and serving. Emphasize how you would measure success through offline metrics and online A/B tests, and how you'd iterate using product analytics.
Pro tip: Anchor your design in Amazon's leadership principles: insist on the highest standards by defining clear success metrics, and think big by considering long-term personalization and scalability. Also, mention how you'd balance exploration and exploitation to avoid feedback loops.
Ask questions to understand the product context, user base, content catalog, and business objectives (e.g., engagement, retention). Define what success looks like with specific metrics.
Outline data sources (user interactions, content metadata, contextual signals) and how you'd process them (batch and real-time) to create features for modeling.
Describe the multi-stage architecture: candidate generation (e.g., collaborative filtering, content-based), ranking (e.g., learning-to-rank), and re-ranking for diversity and business rules.
Explain offline evaluation (e.g., precision@k, NDCG) and online A/B testing methodology, including how to measure impact on key metrics and guardrail metrics.
Discuss how you'd use product analytics to identify improvement opportunities, handle cold start, and scale the system to millions of users and items.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.